import argparse
parser = argparse.ArgumentParser() # 創(chuàng)建一個(gè)解析對(duì)象
parser.add_argument() # 向該對(duì)象中添加你要關(guān)注的命令行參數(shù)和選項(xiàng)
args = parser.parse_args() # 調(diào)用parse_args()方法進(jìn)行解析

import argparse  
def get_options(parser=argparse.ArgumentParser()):  
    parser.add_argument('--workers', type=int, default=0,  
                        help='number of data loading workers, you had better put it '  
                              '4 times of your gpu')  
    parser.add_argument('--batch_size', type=int, default=4, help='input batch size, default=64')  
    parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for, default=10')  
    parser.add_argument('--lr', type=float, default=3e-5, help='select the learning rate, default=1e-3')  
    parser.add_argument('--seed', type=int, default=118, help="random seed")  
    parser.add_argument('--cuda', action='store_true', default=True, help='enables cuda')  
    parser.add_argument('--checkpoint_path',type=str,default='',  
                        help='Path to load a previous trained model if not empty (default empty)')  
    parser.add_argument('--output',action='store_true',default=True,help="shows output")  
    opt = parser.parse_args()  
    if opt.output:  
        print(f'num_workers: {opt.workers}')  
        print(f'batch_size: {opt.batch_size}')  
        print(f'epochs (niters) : {opt.niter}')  
        print(f'learning rate : {opt.lr}')  
        print(f'manual_seed: {opt.seed}')  
        print(f'cuda enable: {opt.cuda}')  
        print(f'checkpoint_path: {opt.checkpoint_path}')  
    return opt  
if __name__ == '__main__':  
    opt = get_options()

$ python config.py
num_workers: 0
batch_size: 4
epochs (niters) : 10
learning rate : 3e-05
manual_seed: 118
cuda enable: True
checkpoint_path:

# 導(dǎo)入必要庫(kù)
...
import config
opt = config.get_options()
manual_seed = opt.seed
num_workers = opt.workers
batch_size = opt.batch_size
lr = opt.lr
niters = opt.niters
checkpoint_path = opt.checkpoint_path
# 隨機(jī)數(shù)的設(shè)置，保證復(fù)現(xiàn)結(jié)果
def set_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
...
if __name__ == '__main__':
  set_seed(manual_seed)
  for epoch in range(niters):
    train(model,lr,batch_size,num_workers,checkpoint_path)
    val(model,lr,batch_size,num_workers,checkpoint_path)

# test.py
import argparse
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--test_action", action='store_true')
    args = parser.parse_args()
    action_val = args.test_action
    print(action_val)

parser.add_argument("--test_action", default='False', action='store_true')

parser.add_argument("--test_action", default='True', action='store_true')